Deep learning for hdr imaging: State-of-the-art and future trends
High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range
of exposures, which is important in image processing, computer graphics, and computer …
of exposures, which is important in image processing, computer graphics, and computer …
Mst++: Multi-stage spectral-wise transformer for efficient spectral reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or
wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB …
wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB …
Deblurring via stochastic refinement
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input
image. However, most existing methods produce a deterministic estimate of the clean image …
image. However, most existing methods produce a deterministic estimate of the clean image …
Deep image deblurring: A survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
Deblur-nerf: Neural radiance fields from blurry images
Abstract Neural Radiance Field (NeRF) has gained considerable attention recently for 3D
scene reconstruction and novel view synthesis due to its remarkable synthesis quality …
scene reconstruction and novel view synthesis due to its remarkable synthesis quality …
Coarse-to-fine sparse transformer for hyperspectral image reconstruction
Many learning-based algorithms have been developed to solve the inverse problem of
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …
Scale-recurrent network for deep image deblurring
In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …
image on different resolutions in a pyramid, is very successful in both traditional optimization …
Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study
This paper introduces a novel large dataset for video deblurring, video super-resolution and
studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges …
studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges …
Human-aware motion deblurring
This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …
between foreground (FG) humans and background (BG). The proposed model is based on a …
Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training
Blind non-uniform image deblurring for severe blurs induced by large motions is still
challenging. Multi-scale (MS) approach has been widely used for deblurring that …
challenging. Multi-scale (MS) approach has been widely used for deblurring that …